Email: ali-burak.uenal [at] uni-tuebingen.de
- Privacy preserving machine learning
- Kernel methods
- Machine learning
- Computational Biology
- Personalized Medicine
- Ph.D. in Computer Science, University of Tübingen, 2022.
Thesis: Towards A Complete Privacy Preserving Machine Learning Pipeline
- M.Sc. in Computer Engineering, Bilkent University, Turkey, 2017.
Thesis: Identification of Cancer Patient Subgroups via Pathway-based Multi-view Graph Kernel Clustering
- B.Sc. in Computer Engineering, Bilkent University, Turkey, 2015.
ESCAPED: Efficient Secure and Private Dot Product Framework for Kernel-based Machine Learning Algorithms with Applications in Healthcare, Association for the Advancement of Artificial Intelligence (AAAI) 2021 Ali Burak Ünal, Mete Akgün, Nico Pfeifer
- Yasin Tepeli*, Ali Burak Ünal*, Furkan Mustafa Akdemir, Öznur Taştan
PAMOGK: a pathway graph kernel-based multiomics approach for patient clustering, Bioinformatics (2020)
Privacy-preserving SVM on Outsourced Genomic Data via Secure Multi-party Computation, March 2020, IWSPA '20: Proceedings of the Sixth International Workshop on Security and Privacy Analytics (Association for Computing Machinery, Pages 61–69)
- Efe Bozkir*, Ali Burak Ünal*, Mete Akgün, Enkelejda Kasneci, and Nico Pfeifer.
Privacy Preserving Gaze Estimation using Synthetic Images via a Randomized Encoding Based Framework
Eye Tracking Research and Applications (ETRA) 2020
- Ali Burak Ünal, Mete Akgün, N. Pfeifer
A framework with randomized encoding for a fast privacy preserving calculation of non-linear kernels for machine learning applications in precision medicine,
CANS 2019, Lecture Notes in Computer Science book series (LNCS, volume 11829)
- Ali Burak Ünal, and Öznur Taştan
Identification of Cancer Patient Subgroups via Smoothed Shortest Path Graph Kernel
NIPS Workshop on Machine Learning in Computational Biology (2016)